1. Jet finding Algorithms at Tevatron B.Andrieu (LPNHE, Paris) On behalf of the collaboration Outline: Introduction The Ideal Jet Algorithm Cone Jet Algorithms: RunII/RunI, D0/CDF k  Jet Algorithm Summary     

2. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -1 Jets: from parton to detector level Higher order QCD processes LO hard processSoft processes QCD partons  jets of hadrons  detector signals factorisation scales in p (p) renormalisation scale PDF of parton i (j ) in p (p) partonic cross section quarks and gluons at high p T produce jets (Sterman&Weinberg, 1977) QCD  Non-perturbative processes not predictable  QCD inspired phenomenology

3. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -2 Jets: from parton to detector level Quark and gluon jets (identified to partons) can be compared to detector jets, if jet algorithms respect collinear and infrared safety (Sterman&Weinberg, 1977) QCD  { High E T jets  “Hard” QCD (non-perturbative effects & scale uncertainty reduced)  Direct insight into parton dynamics  Precise tests of perturbative QCD predictions  Measure  S, constrain proton PDFs, …  Search for new physics Low E T jets  “Soft” QCD (non-perturbative effects & scale uncertainty important)  Test phenomenological models (underlying event, fragmentation)  Study detailed jet structure (jet shapes) Infrared unsafetyCollinear unsafety Figures from hep-ex/0005012

4. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -3 Jet definition Associate “close” to each other “particles”  Clustering (Jet Algorithm) “particles” can be: “close” ?  Distance   R =      or   Y    (preferred in RunII) for Cone Algorithm  relative p T for k  algorithm Calculate jet 4-momentum from “particles” 4-momenta  Recombination scheme invariant under longitudinal boosts  Snowmass scheme (RunI): E T -weighted recombination scheme in ( ,  )  covariant or E-scheme (preferred for RunII): 4-momenta addition used at the end of clustering but also during clustering process (not necessarily the same, still preferrable) Two things need to be done to define a jet: partons (analytical calculations or parton showers MC) “hadrons” = final state particles (MC particles or charged particles in trackers) towers (or cells or preclusters or any local energy deposits) independent of the distance from interaction point invariant under longitudinal boosts

5. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -4 The ideal jet algorithm for pp infrared and collinear safety invariance under longitudinal boosts fully specified and straightforward to implement same algorithm at the parton, hadron and detector level boundary stability (kinematic limit of inclusive jet cross section at E T =  s /2) factorisation (universal parton densities) independence of detector detailed geometry and granularity minimal sensitivity to non-perturbative processes and multiple scatterings at high luminosity minimization of resolution smearing/angle bias reliable calibration maximal reconstruction efficiency (find all jets) vs minimal CPU time replicate RunI cross sections while avoiding theoretical problems Theory Experiment - General Compare jets at the parton, hadron and detector level   Jet algorithms should ensure

6. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -5 Run I Cone Algorithm Based on Snowmass algorithm: E T -weighted recombination scheme in (  ) Preclustering (D0, similar algorithm for CDF) Note: Tower segmentation in (  ) space: D0  0.1 X 0.1, CDF  0.11 X 0.26 start from seeds (= hadronic towers with p T >1 GeV ordered in decreasing p T ) cluster (and remove) all contiguous calorimeter towers around seed in a R= 0.3 cone Clustering start from preclusters (ordered in decreasing E T ) proto-jet candidate = all particles within R cone of the precluster axis in (  ) space CDF: keep towers of the original precluster through all iterations (ratcheting) proto-jet direction compared before/after recombination  iterate until it is stable Merging/Splitting ( treat overlapping proto-jets) E 1  2 > f. Min(E 1,E 2 )  Merge jets E 1  2 < f. Min(E 1,E 2 )  Split jets = assign each particle to its closest jet D0: f = 50 %, use only clusters with E T > 8 GeV - CDF: f = 75 % Final calculation of jet variables (modified Snowmass scheme) scalar addition of E T (D0) or E (CDF) of particles to determine jet E T or E addition of 3-momenta of particles to determine jet direction, then (  ) Note: this procedure is not Lorentz invariant for boosts along beam axis CDF: E T = E sin(  )

7. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -6 Why new algorithms for Run II? Different in D0 and CDF Not infrared and collinear safe due to the use of seeds (collinear safety ensured at sufficiently large E T : E T >20 GeV with p T min (seed) = 1 GeV in D0) Preclustering difficult to match at parton or hadron level CDF ratcheting not modelled in theory Need to introduce a new parameter (R sep ) in jet algorithm at parton level to match theory predictions to measurements (S.D. Ellis et al., PRL69, 3615 (1992)) Not invariant under boosts along beam axis  2 new Cone Algorithms proposed for RunII (G.C. Blazey et al., “RunII Jet Physics”, hep-ex/0005012) Seedless Cone Algorithm RunII (= Improved Legacy or Midpoint) Cone Algorithm  Use k  algorithm (already used in RunI) Run I Cone algorithms have many drawbacks

8. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -7 Seedless Cone Algorithm Not really “seedless”  Use enough seeds (all towers) to find all stable cones First step: form cone around seed, recalculate cone direction (Snowmass recombination) stop processing seed if the cone centroid is outside of the seed tower CDF: use tower size X 1.1 to avoid boundary problems Secund step similar to Run I Cone algorithm: use the cones formed in first step (pre-protojets) as seeds form cone around seed and recalculate cone direction (E-scheme = 4-momentum addition) iterate until cone direction after/before recombination is stable Streamlined (faster) option Stop iteration in second step if the cone centroid is outside of the seed tower  Only miss low E T protojets or stable directions within the same tower  Infrared and collinear safe  Probably close to Ideal for a Cone algorithm  Even the streamlined version is very computational intensive  Use an approximation of Seedless Algorithm  RunII Cone

9. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -8 RunII Cone Algorithm (hep-ex/0005012) QCD calculation at fixed order N  only 2 N –1 possible positions for stable cones (p i, p i +p j, p i +p j +p k,…) Data: consider seeds used in RunI Cone algorithms as partons  in addition to seeds, use ‘midpoints’ i.e. p i +p j, p i +p j +p k,… only need to consider seeds all within a distance  R < 2R cone only use midpoints between proto-jets (reduce computing time) otherwise algorithm similar to RunI How to build a valid approximation of the seedless algorithm? E-scheme recombination = 4-momenta addition use true rapidity Y instead of pseudo-rapidity  in  R use all towers as seeds (p T > 1 GeV) splitting/merging: p T ordered, f = 50 % Other specifications of the suggested RunII cone Algorithm

10. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -9 D0 Run II Cone Algorithm: Preclustering Simple Cone Algorithm Start from particles with highest p T and p T >500 MeV Precluster formed from all particles within a cone of r =0.3 (r =0.2) for Cone jets with R  0.5 (R = 0.3) (  RunI: only neighbouring cells) Remove particles as soon as they belong to a precluster No cone drifting Precluster 4-momentum calculated using the E-scheme

11. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -10 Abort drifting if: p T < 0.5 Jet p T min # Iterations = 50 (avoids infinite cycles) D0 Run II Cone Algorithm: Clustering Use all preclusters as seeds (p T ordered), except those close to already found protojets (  R (precluster,protojet)< 0.5 R cone ) Repeat same clustering for midpoints* except: No condition on close protojet No removal of duplicates Cone drifting until cone axis coincides with jet direction Remove duplicates * - only pairs are considered - calculated using p T –weighted mean

12. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -11 D0 Run II Cone Algorithm: Merge/Split Use p T ordered list of proto-jets (from seeds and midpoints) If some energy is shared between two proto-jets, decide to split/merge depending on shared fraction Re-order list of merged/splitted jets Recalculate 4-momenta of merged/splitted jets

13. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -12 The Smaller Search Cone Algorithm Jets might be missed by RunII Cone Algorithm (S.D. Ellis et al., hep-ph/0111434)  low p T jets too close to high p T jet to form a stable cone (cone will drift towards high p T jet) too far away from high p T jet to be part of the high p T jet stable cone proposed solution remove stability requirement of cone run cone algorithm with smaller cone radius to limit cone drifting (R search = R cone /  2) form cone jets of radius R cone around protojets found with radius R search Problem of lost jets seen by CDF, not seen by D0  A physics or an experimental problem? Proposed solution not satisfactory in terms of elegance and simplicity  D0 prefers using RunII Cone without Smaller Search Cone Remarks

14. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -13 k  Algorithm originally proposed for e + e - colliders, then adapted to hadron colliders (S. Catani et al., NPB406,187 (1993)) universal factorisation of initial-state collinear singularities infrared safe: soft partons are combined first with harder partons  result stable when energy of soft partons -> 0 collinear safe: two collinear partons are combined first in the original parton no issue with merging/splitting p T ordered list of particles  form the list of d i = (p T i ) 2 calculate for all pairs of particles, d i j = Min((p T i ) 2, (p T j ) 2 )  R/D find the minimum of all d i and d i j if it is a d i, form a jet candidate with particle i and remove i from the list if not, combine i and j according to the E-scheme use combined particle i + j as a new particle in next iteration need to reorder list at each iteration  computing time  O (N 3 ) (N particles) proceed until the list of preclusters is exhausted Description of inclusive k  algorithm (Ellis&Soper, PRD48, 3160, (1993)) Remarks

15. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -14 D0 Run II k  Algorithm Use E -scheme for recombination Use p T ordered list of preclusters (geometrical 2x2 preclustering) Remove preclusters with E < 0 Either merge pairs of preclusters which are closest to each other in relative p T or form a jet with each isolated low p T precluster When all preclusters have been associated to a jet, calculate 4-momenta of all jets Apply a p T min cut on jets (p T > 8 GeV)

16. ISMD04, Sonoma, 28 Jul. 2004 B.Andrieu Jet finding algorithms at Tevatron -15 Summary RunII (Midpoint) Cone Algorithm clear improvement over RunI Algorithm Many problems or questions still remain open (not exhaustive list) : D0 uses only RunII Cone (Midpoint) Algorithm (no smaller search cone) CDF still uses JetClu (RunI) Cone Algorithm + Smaller Search Cone Algorithm D0 implementation does not fully follow RunII Cone recommendations p T min / 2 cut on proto-jets candidates preclustering seeds too close to already found protojets not used influence of parameters for precluster formation? usefulness of a p T cut on proto-jets before merging/splitting at high luminosity? procedure chosen for merging/splitting optimal? origin of the difference D0 vs CDF for lost jets problem? In contrast, k  algorithm is conceptually simpler, theoretically well- behaved, although less intuitive. It also needs studies, as for the RunII Cone Algorithm (jet masses, sensitivity to experimental effects, …).  However, shouldn’t we put more effort on using k  algorithm and less on reproducing results obtained with RunI algorithms? (personal statement)